Project Summary Abundant research demonstrates that early detection of cancer leads to improved patient prognosis. By detecting cancer earlier, when tumors are in their primary stages, treatment can be applied before metastases have occurred. The presence of microcalcifications (MCs) is indicative of malignancy in the breast and improving the ability to detect MCs with modern imaging technology remains an open question. The presence of MCs is associated with presence of cancer in the breast, i.e., 30-50% of all nonpalpable breast cancers detected using mammograms are based on identifying the presence of MCs. Therefore, improving the sensitivity of imaging techniques to detect MCs in the breast will provide an important role for the early detection and diagnosis of breast cancer. Recently, we developed a novel nonlinear beamforming technology for ultrasonic arrays that provides super resolution of ultrasonic images (up to 25 times improvements in resolution). The beamforming technique, called null subtraction imaging (NSI), utilizes nulls in the beam pattern to create images using ultrasound. Lateral resolution gains provided by NSI are accompanied by a reduction in side lobes present in all beam patterns and increases in the signal-to-noise ratio (SNR). Ultrasonic images constructed with NSI result in suppression of speckle artifacts and an intensification of singular targets. Therefore, we hypothesize that NSI imaging will perform well for the specific imaging task of detection of MCs in tissues. We will develop and validate NSI for imaging and detecting MCs in an animal model of breast cancer through two aims. In the first aim we will quantify the ability of NSI to detect MCs in an animal model compared to conventional ultrasonic and X-ray imaging techniques. We hypothesize that the use of NSI will result in a quantifiably improved detection of MCs in animal models compared to conventional ultrasound approaches and X-ray imaging. Conventional ultrasound approaches use delay and sum to do beam formation and can use different signal processing tools to improve MC detection. Conventional ultrasound B-mode imaging, NSI imaging and X-ray CT will be used to detect MCs in a rat model of breast cancer and their detection performance (sensitivity and specificity) will be compared. In the second aim we will develop and validate approaches on receive to increase the density of scan lines when using NSI. We hypothesize that the scan line density can be sufficiently increased using NSI without physically translating the transducer. Because the imaging beam associated with NSI is so narrow, conventional linear sequential scanning techniques that translate a beam at each step by one pitch cause spaces between the beams that are not interrogated. To ensure that no tissue region is missed during scanning, it is necessary to increase the density of beams interrogating tissue. This can be accomplished on receive by using conventional methods to increase scan line density, i.e., interpolation or gird focusing.